Facilitating noise estimation in wireless communication

ABSTRACT

Providing for noise estimation in wireless communication, and particularly for access request signals transmitted by a user equipment (UE), is described herein. By way of example, a wireless signal receiver can employ unused signal dimensions of a wireless network for noise estimation. In addition, the unused signal dimensions can be selected for time-frequency resources that are associated with a particular wireless channel, in order to obtain a noise estimate for that channel. By employing unused signal dimensions, a noise measurement is likely to include no other signal transmissions, and provide an accurate estimate of noise on that channel. According to various aspects of the subject disclosure, one or more Chu sequences employed for signal transmissions, root sequences thereof, or one or more cyclic shifts of a root sequence can be employed for the unused signal dimension.

CLAIM OF PRIORITY UNDER 35 U.S.C. §119 AND §120

The present Application is a continuation application of U.S. patentapplication Ser. No. 12/834,554, filed Jul. 12, 2010, entitledFACILITATING NOISE ESTIMATION IN WIRELESS COMMUNICATION which claims thebenefit to U.S. Provisional Patent Application No. 61/226,149, filedJul. 16, 2009, entitled METHOD AND APPARATUS FACILITATING NOISEESTIMATION, assigned to the assignee hereof, and hereby expresslyincorporated by reference herein by their entireties.

BACKGROUND

I. Field

The present aspects relate to wireless communication, and moreparticularly, to techniques for noise estimation, such as, but notlimited to, physical random access channel (PRACH) noise estimation.

II. Background

Wireless communication systems are widely deployed to provide varioustypes of communication content, such as voice content, data content, andso on. Typical wireless communication systems can be multiple-accesssystems capable of supporting communication with multiple users bysharing available system resources (e.g., bandwidth, transmit power,etc.). Examples of such multiple-access systems may include codedivision multiple access (CDMA) systems, time division multiple access(TDMA) systems, frequency division multiple access (FDMA) systems,orthogonal frequency division multiple access (OFDMA) systems, and thelike. Additionally, the systems can conform to specifications such asthird generation partnership project (3GPP), 3GPP2, High Speed PacketAccess (HSPA), High Speed Downlink Packet Access (HSDPA), High SpeedUplink Packet Access (HSUPA), 3GPP long-term evolution (LTE), LTEAdvanced (LTE-A), etc.

Generally, wireless multiple-access communication systems cansimultaneously support communication for multiple mobile devices. Eachmobile device can communicate with one or more base stations viatransmissions on forward and reverse links. The forward link (ordownlink) refers to the communication link from base stations to mobiledevices, and the reverse link (or uplink) refers to the communicationlink from mobile devices to base stations. Further, communicationsbetween mobile devices and base stations can be established viasingle-input single-output (SISO) systems, multiple-input single-output(MISO) systems, multiple-input multiple-output (MIMO) systems, and soforth.

To effect wireless communications, a user equipment requests access toenter a wireless network by transmitting a wireless signal on an accesschannel of the wireless network. The wireless signal is received by abase station and analyzed to determine whether to grant the userequipment access to the wireless network (e.g., if the user equipment isassociated with a valid subscription to the wireless network). In orderto successfully receive and demodulate a received signal, the basestation must separate the received signal from other transmitted signalsand compare received signal strength to noise on the access channel,among other things. However, noise estimation can be difficult where thesignal strength is relatively weak compared to the noise, whereinterference from other transmitters exists, or where other physicalconditions such as signal scattering, high Doppler effects, or the like,are prevalent on the access channel. Accordingly, mechanisms foraccurately estimating and removing noise are beneficial to successfulwireless communication.

SUMMARY

The following presents a simplified summary of one or more aspects inorder to provide a basic understanding of such aspects. This summary isnot an extensive overview of all contemplated aspects, and is intendedto neither identify key or critical elements of all aspects nordelineate the scope of any or all aspects. Its sole purpose is topresent some concepts of one or more aspects of the subject disclosurein a simplified form as a prelude to the more detailed description thatis presented later.

The subject disclosure provides for noise estimation in wirelesscommunication, and particularly for access request signals transmittedby user equipment (a UE). In some aspects of the subject disclosure, areceiver can employ unused signal dimensions for noise estimation. Inaddition, the unused signal dimensions can be selected fortime-frequency resources that are associated with a particular wirelesschannel, in order to obtain a noise estimate for that channel. Byemploying unused signal dimensions, a noise measurement is likely toinclude no other signal transmissions, and provide an accurate estimateof noise on that channel.

According to particular aspects of the subject disclosure, various typesof unused signal dimensions can be employed for channel noiseestimation. For instance, one or more unused cyclic shifts of aZadoff-Chu sequence (referred to herein as a Chu sequence) can beemployed as the unused signal dimension. In another instance, a subsetof Chu sequences can be reserved for noise estimation, either by ahardware signal processor that generates energy correlations fordifferent cyclic shifts of these sequences, or a software processingmodule that employs an energy correlation output of the hardware signalprocessor. In yet another instance, one or more roots of a Chusequence(s) can be reserved for noise estimation instead. According toat least one aspect, a root sequence can be selected that is nototherwise utilized by a receiving base station, or by neighboring basestations within a signaling range of the receiving base station.Depending on a number of unused signal dimensions employed for noiseestimation, or whether estimation is performed at the hardware signalprocessor or the software processing module, an average of multipleestimates can be performed across multiple cyclic shifts to increaseoverall accuracy of the noise estimate.

In other aspects of the subject disclosure, provided is a method forwireless communication. The method can comprise identifying a set oftime-frequency resources for a random access probe according to awireless network protocol. Additionally, the method can compriseidentifying at least one unused dimension that is orthogonal orpseudo-orthogonal to permitted random access probe dimensions. Furtherto the above, the method can comprise estimating noise for the randomaccess probe based on analysis of the at least one unused dimension.

In an additional aspect, provided is an apparatus configured forwireless communication. The apparatus can comprise a communicationinterface for receiving an uplink wireless signal. The apparatus canalso comprise a memory for storing instructions configured to measurenoise for the uplink wireless signal and a data processor for executingmodules that implement the instructions. At least one of the modules cancomprise a reference module that identifies a set of unused signaldimensions for a geographic region in which the apparatus supportswireless network service, wherein the set of unused signal dimensionsare time-based or frequency-based signal dimensions. Moreover, themodules can also comprise a calculation module that measures receivedenergy on the set of unused signal dimensions and computes an estimateof noise for the uplink wireless signal.

In still other aspects, disclosed is an apparatus for wirelesscommunication. The apparatus can comprise means for identifyingtime-frequency resources provided for uplink random access requestsaccording to a wireless network protocol. Further, the apparatus cancomprise means for identifying signal dimensions that are orthogonal orpseudo-orthogonal to permitted random access dimensions on thetime-frequency resources, and that are not assigned for uplinktransmission on the time-frequency resources in a geographic regionserved by the apparatus. Further still, the apparatus can comprise meansfor estimating noise on the time-frequency resources based on analysisof the signal dimensions.

According to another aspect, provided is at least one processorconfigured for wireless communication. The processor(s) can comprise afirst module that identifies time-frequency resources provided foruplink random access requests according to a wireless network protocol.In addition, the processor(s) can comprise a second module thatidentifies signal dimensions that are orthogonal or pseudo-orthogonal topermitted random access dimensions on the time-frequency resources, andthat are not assigned for uplink transmission on the time-frequencyresources in a geographic region served by a base station. Furthermore,the processor(s) can comprise a third module that estimates noise on thetime-frequency resources based on analysis of the signal dimensions.

Still other aspects of the subject disclosure provide a computer programproduct comprising a computer-readable medium. The computer-readablemedium can comprise a first set of codes for causing a computer toidentify time-frequency resources provided for uplink random accessrequests according to a wireless network protocol. Further, thecomputer-readable medium can comprise a second set of codes for causingthe computer to identify signal dimensions that are orthogonal orpseudo-orthogonal to permitted random access dimensions on thetime-frequency resources, and that are not assigned for uplinktransmission on the time-frequency resources in a geographic regionserved by a base station. Further still, the computer-readable mediumcan comprise a third set of codes for causing the computer to estimatenoise on the time-frequency resources based on analysis of the signaldimensions.

To the accomplishment of the foregoing and related ends, the one or moreaspects comprise the features hereinafter fully described andparticularly pointed out in the claims. The following description andthe annexed drawings set forth in detail certain illustrative featuresof the one or more aspects. These features are indicative, however, ofbut a few of the various ways in which the principles of various aspectsmay be employed, and this description is intended to include all suchaspects and their equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example wireless apparatus thatprovides noise estimation for received wireless signals according todisclosed aspects.

FIG. 2 illustrates a block diagram of a sample noise estimationapparatus according to particular aspects of the subject disclosure.

FIG. 3 illustrates a diagram of an example selection of Chu sequencesfrom which unused signal dimensions can be employed for noiseestimation.

FIG. 4 illustrates a block diagram of a sample base station thatprovides noise estimation for a physical random access channel (PRACH).

FIG. 5 illustrates a flowchart of an example methodology for providingimproved noise estimation in wireless communication according to otheraspects.

FIGS. 6 and 6A illustrate a flowchart of a sample methodology foremploying unused signal dimensions for PRACH noise estimation.

FIG. 7 illustrates a block diagram of a sample apparatus that providesnoise estimation according to still other aspects of the subjectdisclosure.

FIG. 8 illustrates a block diagram of a sample wireless communicationsapparatus that can be employed to implement various aspects of thesubject disclosure.

FIG. 9 illustrates a block diagram of a sample cellular environment forwireless communications according to further aspects.

FIG. 10 illustrates a block diagram of an example cell-based wirelesscommunication arrangement suitable for one or more disclosed aspects.

DETAILED DESCRIPTION

Various aspects are now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of one or more aspects. It can be evident, however, thatsuch aspect(s) can be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate describing one or more aspects.

In addition, various aspects of the disclosure are described below. Itshould be apparent that the teaching herein can be embodied in a widevariety of forms and that any specific structure and/or functiondisclosed herein is merely representative. Based on the teachings hereinone skilled in the art should appreciate that an aspect disclosed hereincan be implemented independently of any other aspects and that two ormore of these aspects can be combined in various ways. For example, anapparatus can be implemented and/or a method practiced using any numberof the aspects set forth herein. In addition, an apparatus can beimplemented and/or a method practiced using other structure and/orfunctionality in addition to or other than one or more of the aspectsset forth herein. As an example, many of the methods, devices, systemsand apparatuses described herein are described in the context ofproviding improved noise estimation for wireless communication, amongother things. One skilled in the art should appreciate that similartechniques could apply to other communication environments.

Wireless communication systems achieve electronic communication betweenremotely located wireless nodes through local infrastructure deployments(e.g., comprising a network of base stations) and central networks thatcommunicatively couple local infrastructure. The central networks areconfigured to determine location of a mobile user equipment (UE) todeliver content to the UE, and in the aggregate form an overallcommunication link between the local infrastructure serving individualmobile devices and remote nodes engaged in the electronic communication.In general, the local infrastructure can utilize various wirelesscommunication technologies to exchange wireless information with thesenodes, including code division multiplex (CDM), frequency divisionmultiplex (FDM), orthogonal frequency division multiplex (OFDM), timedivision duplex (TDD), and so on, utilizing various single or multiplecarrier frequencies, or mixed single and multiple carrier frequencies.Furthermore, the infrastructure may include 3GPP LTE or LTE-A wirelessnetworks, WCDMA wireless networks, OFDMA wireless networks, CDMAnetworks, 3GPP2 CDMA2000 networks, EV-DO networks, WiMAX networks, HSPAnetworks, etc.

In order to access a wireless network, a UE sends, transmits orotherwise communicates an access probe signal to a base station of thewireless network. Generally, the access probe signal is transmitted onparticular wireless resources (e.g., time-frequency resources) reservedby the wireless network for access signals (these wireless resources canbe referred to as access resources, or an access channel, e.g., aphysical random access channel (PRACH)). Access channel resources aregenerally broadcast by the base station to a cell, or geographic region,served by the base station. This broadcast can comprise a systeminformation block (a SIB) broadcast, or the like. A device configured toreceive the broadcast and transmit a suitable access probe on the accesschannel resources can attempt to access the wireless network.

Access probes are generally referred to as blind access signals, orrandom access signals, because the base station is typically not awareof whether it will receive an access probe signal on an access resourceor how many access probe signals it will receive. The base station maydetermine that a remote device (the UE) is attempting to access thewireless network by measuring received energy levels on the accessresource, and comparing the received energy levels to a predeterminedthreshold. For instance, the base station can receive a set of signalson the access resource, and determine that signals having an energylevel above the predetermined threshold are access probe signals, andsignals having an energy level below the predetermined threshold arenoise. Since the base station does not have prior knowledge of whetheror how many UEs are attempting to access the wireless network on anaccess channel, the base station may attribute all received signals tonoise, if energy levels of those signals do not exceed the predeterminedthreshold. Therefore, the base station will not respond to an accessprobe that is not strong enough to be attributed as a valid signal.

It can be appreciated that if the predetermined threshold is set toolow, then a base station may respond in a case where no UE is attemptingto access the wireless network. As a consequence, the base station canmake inefficient use of network resources, and unnecessarily interferewith other signals of nearby base stations. Additionally oralternatively, if the predetermined threshold is set too high, then thebase station can ignore valid access requests, resulting in poorwireless service. In theory, the predetermined threshold should be afunction of actual noise levels affecting an access channel at a giventime. However, the actual noise is typically unknown and is estimatedinstead. Accordingly, accurate noise estimation for a PRACH or otherchannel is helpful for determining a false alarm threshold (a FAthreshold) employed to reject or respond to potential access requests.Described herein are various techniques for noise estimation, includingPRACH noise estimation, in accordance with aspects of the subjectdisclosure.

Referring now to the figures, FIG. 1 depicts a block diagram of anexample wireless communication apparatus 100 according to aspects of thesubject disclosure. Wireless communication apparatus 100 can be employedfor receiving and responding to random access requests of UEs attemptingto access a wireless network. In addition, wireless communicationapparatus 100 can provide noise estimation to differentiate valid accessrequests from background noise on an access channel, such as a PRACH.

One mechanism that wireless networks utilize to orthogonalize orpseudo-orthogonalize wireless signals is to employ Chu sequences. A Chusequence is a complex-valued mathematical sequence which can be appliedto a radio signal and results in a substantially constant amplitudesignal. Further, cyclic shifted versions of the Chu sequence and theradio signal are pseudo-orthogonal when received by a receiver. Agenerated Chu sequence that is not shifted is a root sequence. Asdescribed herein, various subsets of a set of Chu sequences, includingone or more sequences, one or more root sequences, or one or more cyclicshifts can be employed for noise estimation by wireless communicationapparatus 100 on an access channel.

Wireless communication apparatus 100 can comprise a base station 102 ofa wireless network, communicatively coupled with a noise estimationapparatus 104. It should be appreciated that base station 102 can be anaccess point of a wireless network, such as a 3GPP LTE network, or otherfrequency division multiple access network (an FDMA network), or asimilar network. Further, base station 102 can employ single or multiplecarrier frequencies on an uplink or a downlink.

In 3GPP LTE, for example, a PRACH employs Chu sequences due tobeneficial correlation properties and zero (or substantially zero)correlation within the same root sequence. Neighboring cells (e.g.,within a wireless signal range of base station 102) can employ differentroot sequences to mitigate inter-cell interference. To filter signalstransmitted with Chu sequences, base station 102 receives a signal, andremoves a cyclic prefix from an extracted and down-sampled time domainversion of the received signal. Signal samples are converted into thefrequency domain (optionally with multi-segment processing, wheremultiple segments comprising multiple time domain samples are generated,to correct for high Doppler conditions, for instance) and matched withChu sequences generated in the frequency domain. Matched output symbolsare converted back to the time domain to obtain energy correlationmetrics (also referred to herein as energy metrics) within a rootsequence. Generally, a single cyclic cross-correlation is obtained perroot sequence (e.g., for low Doppler and small frequency offsetconditions), however multiple cyclic cross-correlations can also beobtained per root sequence for multi-segment processing. Energy levelsobtained from the cyclic cross-correlation can be employed to estimatethe received signal. As is described below, various unused signaldimensions, on which no valid access signals are expected, can beemployed to estimate access channel noise independent of transmittedaccess signals.

Noise estimation apparatus 104 can comprise a communication interface106 for receiving an uplink wireless signal on an uplink channel (e.g.,a PRACH) employed by base station 102. In one aspect of the subjectdisclosure, communication interface 106 can comprise a transmit-receivechain of base station 102 (e.g., see FIG. 8, infra). In other aspects ofthe subject disclosure, communication interface 106 can instead comprisean electronic communication interface suitable to communicate with thetransmit-receive chain of base station 102 and, for instance, receivethe uplink wireless signal from base station 102.

In addition to the foregoing, noise estimation apparatus 104 cancomprise a memory 108 for storing instructions configured to estimatenoise for uplink wireless signal transmissions and a data processor 110for executing modules that implement the instructions. These modules caninclude, for instance, a reference module that identifies a set ofunused signal dimensions for the uplink channel. In addition to theforegoing, noise estimation apparatus 104 can comprise a calculationmodule 116 that measures received energy on the set of unused signaldimensions and computes an estimate of noise for the uplink wirelesssignal.

Unused signal dimensions can be identified by reference module 112 forthe noise estimation according to various suitable network protocols.For instance, the unused signal dimensions can be extracted from a setof signal sequences 114 stored in memory 108, based on a protocol (notdepicted—but which also can be stored in memory 108) that assigns signaldimensions to channels employed by base station 102. Particularly,signal dimensions that are not assigned by the protocol can be employedby reference module 112. In one instance, the set of unused signaldimensions can comprise a set of code sequences that are not employedfor transmission at least of uplink signals in a geographic regionserved by noise estimation apparatus 104 and base station 102. As anexample, the code sequences can be a subset of Chu sequences, or one ormore root sequences, that are not employed for providing orthogonalityor pseudo-orthogonality at least for the uplink signals. In anotherinstance, the set of unused signal dimensions can comprise unused cyclicshifts of a set of Chu sequences that are in part employed by basestation 102. As still another example, the set of unused signaldimensions can comprise a set of cyclic shifts of a root sequence thatis not employed for generating uplink signal transmissions in thegeographic area, or within a signal range of base station 102. In thislatter case, reference module 112 identifies the set of unused signaldimensions from cyclic shifts of the unused root sequence, andcalculation module 116 obtains a set of energy metrics per cyclic shiftof the unused root sequence and averages the set of energy metrics tocompute the estimate of noise.

FIG. 2 illustrates a block diagram of an example noise estimationapparatus 200 according to further aspects of the subject disclosure. Inat least some aspects of the subject disclosure, noise estimationapparatus 200 can be substantially similar to noise estimation apparatus104 of FIG. 1, supra. However, the subject disclosure is not so limited.As is described herein and below, noise estimation apparatus 200 cancomprise some or all of the features of noise estimation apparatus 104,as well as additional features.

Noise estimation apparatus 200 can comprise a receiver module 202configured to receive wireless signals. Particularly, receiver module202 can be configured to identify access channel resources associatedwith a PRACH (e.g., time-frequency resources designated for randomaccess probe signals) of a wireless network, and receive and down-samplesignal energy observed on the PRACH. Additionally, noise estimationapparatus 200 can comprise memory 204 for storing instructions orprotocols configured to estimate noise on the PRACH, and a dataprocessor 206 that executes one or more modules of noise estimationapparatus 200 to implement features thereof. Particularly, a referencemodule 208 can be executed to identify a set of unused signal dimensionsfrom signal sequences 210 stored in memory 204. These unused signaldimensions can be sequences, root sequences, or cyclic shifts of one ormore sequences that are not employed within a region served by noiseestimation apparatus 200 or within a signal range of a base stationcoupled with noise estimation apparatus 200. Because these signaldimensions are unused, noise estimation apparatus 200 can assume thatobserved energy according to these signal dimensions on the PRACH arenoise.

Once identifies, reference module 208 provides the unused signaldimensions to a calculation module 212. Calculation module 212 comprisesa hardware signal processor 214 that measures received energy on the setof unused signal dimensions. The received energy can be employed toestimate noise on the PRACH. In one instance, the estimate of noise canbe performed by hardware signal processor 214. In another instance, theestimate of noise can be performed by a processing module 218, which cancomprise hardware, software, firmware, or a suitable combinationthereof. Noise estimation performed by hardware signal processor 214 canprovide improved accuracy of the noise, by averaging multiple energylevels of multiple cyclic shifts. Noise estimation performed byprocessing module 218 can provide noise estimation with few or nochanges to hardware of noise estimation apparatus 200, which can beadapted to existing receiver devices (e.g., of a base station) with fewor no hardware changes, and minimal time for reconfiguration.

In one aspect of the subject disclosure, hardware signal processor 214can be employed to compute the estimate of noise by measuring a metricof received signal energy for a subset of unused signal dimensions ofthe set of unused signal dimensions provided by reference module 208. Asan illustrative example of noise estimation, hardware signal processor214 generates a dimension metric for each unused signal dimension of thesubset of unused signal dimensions. After generating these dimensionmetrics, hardware signal processor 214 performs a statisticalcalculation on each dimension metric to derive an aggregated metric ofthe subset of unused signal dimensions. In at least one particularaspect, the statistical calculation can be an average of the dimensionmetrics, a mean of the dimension metrics, a mode of the dimensionmetrics, or the like, or a suitable combination thereof. Once generated,hardware signal processor 214 employs the aggregated metric for theestimate of noise, which can be stored in an estimate of noise file 220in memory 204. Because hardware signal processor 214 directly observesenergy levels for the subset of unused signal dimension, the estimate ofnoise generated by hardware signal processor 214 can be averaged acrossmultiple signal dimensions, for instance, resulting in a higher accuracyestimate.

In another aspect of the subject disclosure, processing module 218 canbe employed to compute the estimate of noise. In one illustrativeexample of this aspect, processing module 218 employs a signalprocessing formula 216 stored in memory 204 to compute the estimate ofnoise. In one instance, this estimate of noise can be based on a highestenergy metric output by hardware signal processor 214 from a set ofcyclic shift energy metrics (e.g., wherein the unused signal dimensionscomprise a set of unused cyclic shifts of a Chu sequence(s) or rootsequence(s) that is at least in part employed for signal transmission ina geographic region). In this case, the highest energy metric can beemployed directly for the estimate of noise. In yet another instance,this estimate of noise can be computed from an aggregate measurement ofthe received energy on the set of unused signal dimensions. In thislatter case, the set of unused signal dimensions can be derived from aset of Chu sequences or a set of root sequences that are not utilizedwithin the geographic region (or a neighboring region), or a wirelessrange of a base station associated with noise estimation apparatus 200.Hardware signal processor 214 outputs a set of highest energy metrics,at least one for each sequence of the set of Chu sequences or rootsequences, which are received at processing module 218. Signalprocessing formula 216 then performs an average (or other suitablestatistical calculation) of a set of received energy metrics associatedwith the set of unused signal dimensions. Furthermore, processing module218 employs the average of the set of received energy metrics for theestimate of noise, which can be saved in the estimate of noise file 220in memory 204.

Once determined, an estimate of noise can be provided to a base station(not depicted) coupled with noise estimation apparatus 200. Thisestimate of noise can be utilized to establish a FA threshold for thePRACH. Accordingly, signal energy levels higher than the FA thresholdare deemed to be valid access probe requests, and signal energy levelslower than the FA threshold are deemed to be invalid access proberequests, or noise.

FIG. 3 illustrates a diagram of code sequences 300 that can be employedat least in part as unused signal dimensions for estimating noise withina cell of a wireless network. Particularly, code sequences 300 cancomprise a set of ‘Y’ Chu sequences, where ‘Y’ is a positive integer. Inat least one aspect of the subject disclosure, Y=64 Chu sequences,although more or fewer Chu sequences can be employed in various aspectsof the subject innovation. As depicted, code sequences 300 comprise aset of Y root sequences, comprising root sequence 1 302A, root sequence2 302B, through root sequence Y 302C (referred to collectively as rootsequences 302A-302C). Root sequences 302A-302C are prime sequences thatcomprise a prime number (e.g., 2, 3, 5, 7, 11, and so on) ‘X’ ofcomplex-number values A_(1, 2, . . . X), B_(1, 2, . . . X),C_(1, 2, . . . X), respectively. In at least one aspect, X can be 839,although the subject disclosure is not so limited. Further, in anotheraspect, sequences 302A-302C can comprise different prime numbers ofvalues, e.g., X, N and O, where N and O are different prime numbers thanX.

Each root sequence 302A-302C can be cyclically shifted ‘X−1’ number oftimes to generate X−1 shifted sequences, plus a root sequence, for atotal of X sequences per root sequence 302A, 302B, 302C. Particularly,root sequence 302A comprises root sequence 1 302A, cyclic shift sequence1 304B, cyclic shift sequence 2 304C, through cyclic shift sequence X−1304D, for a total of X sequences. Different root sequences 302A-302C andcyclic shifted sequences thereof (e.g., cyclic shifted sequences304B-304D of root sequence 302A) can be employed for generatingpseudo-orthogonal signal dimensions for wireless signals, as describedherein. Furthermore, cyclic shifts (e.g., cyclic shift sequence 2 304C)of a root sequence (e.g., root sequence 1 302A) that are not assigned totransmission of signals can be employed as unused signal dimensions 306for noise estimation as described herein, even if a corresponding rootsequence or other cyclic shifts of the root sequence are employed forsignal transmissions.

Additionally, in some aspects of the subject disclosure, one or moreroot sequences can be reserved for noise estimation. In these aspects,the root sequence(s) as well as cyclic shifted variations thereof can beemployed for the unused signal dimensions. One advantage of employing aset of root sequences is that conventional receiver hardware can outputan energy metric for each root sequence. Where multiple root sequencesare reserved for noise estimation, multiple energy metrics (one perroot) are output by conventional receiver hardware, enabling an averageof these energy metrics per root sequence to be performed in software,providing improved noise estimation with limited changes to conventionalreceivers. Alternatively, suitably configured receiver hardware canperform an average of energy metrics for each cyclic shift per reservedroot sequence, resulting in an average over a wide range of signaldimensions. This suitably configured receiver hardware can thereforeprovide a highly accurate estimate of noise. According to still otheraspects of the subject disclosure, a set of Chu sequences 302 can bereserved for noise estimation, where each root sequence and cyclicshifted sequence of the set of Chu sequences 302 can be employed forenergy metric averaging. Table 1 below indicates an example cyclic shiftconfiguration employed for some 3GPP LTE standards, illustratingexamples of unused cyclic shift dimensions for various cyclic shiftconfigurations, N_(CS).

TABLE 1 Table: N_(CS) for preamble generation (preamble formats 0-3).N_(CS) Unrestricted configuration set N_(CS) value Number of Unusedshifts 0 0 Leave some shifts per sequence (i.e., do not use up allshifts per sequence) 1 13 7 from the 1^(st) root 2 15 700 from the 2ndroot 3 18 500 from the 2nd root 4 22 250 from the 2nd root 5 26 7 fromeach of the 2 roots (14 in total) 6 32 450 from the 3^(rd) root 7 38 70from the 3^(rd) root 8 46 370 from the 4^(th) root 9 59 360 from the5^(th) root 10 76 150 from the 6^(th) root 11 93 740 from the 8^(th)root 12 119 720 from the 10^(th) root 13 167 170 from the 13^(th) root14 279 560 from the 22^(nd) root 15 419 Leave some shifts per sequence(i.e., do not use up all shifts per sequence)

FIG. 4 illustrates a block diagram of an example wireless communicationsystem 400 comprising a base station 402, according to further aspectsof the subject disclosure. Base station 402 can be configured to providecell access for UEs 404 attempting to acquire a wireless network.Particularly, base station 402 can be configured to provide noiseestimation for PRACH channels employed by base station 402 for networkaccess requests. The PRACH channels can comprise, for instance, a set oftime-frequency resources of the wireless network that are dedicated to,or employed for, random access requests by UE(s) 404. Particularly, thenoise estimation can be based on energy metrics on unused signaldimensions associated with the PRACH channels. As is described herein,base station 402 can implement the noise estimation in hardware,software, or a combination thereof, to provide a tradeoff betweenminimal/cost effective changes to conventional signal receivers andproviding high accuracy estimations of the noise.

Base station 402 (e.g., access point, . . . ) can comprise a receiver410 that obtains wireless signals from UE(s) 404 through one or morereceive antennas 406, and a transmitter 430 that sends coded/modulatedwireless signals provided by modulator 428 to UE(s) 404 through atransmit antenna(s) 408. Receive antenna(s) 406 and transmit antenna(s)408, along with receiver 410 and transmitter 430, can comprise a set ofwireless transceivers for implementing wireless data exchange with UE(s)404, as described herein.

Receiver 410 can obtain information from receive antennas 406 and canfurther comprise a signal recipient (not shown) that receives uplinksignals transmitted by UE(s) 404. Additionally, receiver 410 isoperatively associated with a demodulator 412 that demodulates receivedinformation. Demodulated symbols are analyzed by a data processor 414.Data processor 414 is coupled to a memory 416 that stores informationrelated to functions provided or implemented by base station 402.

In addition to the foregoing, base station 402 can comprise a noiseestimation apparatus 418. According to one or more aspects of thesubject disclosure, noise estimation apparatus 418 can be substantiallysimilar to noise estimation apparatus 104 of FIG. 1, or noise estimationapparatus 200 of FIG. 2, or a combination thereof. It should beappreciated that noise estimation apparatus 418 is not limited to theseaspects, however. In operation, noise estimation apparatus 418 cancomprise a reference module 420 that identifies a set of unused signaldimensions from at least a subset of code sequences employed by basestation 402 for signal transmission on a downlink to UE(s) 404, or on anuplink from UE(s) 404. Particularly, the set of unused signal dimensionscan comprise a set of Chu sequences, or a set of root sequences of oneor more Chu sequences, reserved for noise estimation. Cyclic shifts ofthis sequence(s)/root sequence(s) can be analyzed by a calculationmodule 422, which employs energy metrics of time-frequency resourcesassociated with a PRACH employed for UE(s) 404. In one instance,calculation module 422 employs a hardware processor 424 to obtain theseenergy metrics and compute noise on the PRACH. In another instance,calculation module 422 employs a software processing module 426 toestimate noise from one or more energy metrics output by hardwareprocessor 424. Based on the energy metrics, or a statistical averagingthereof, an estimate of noise can be generated for the PRACH. As oneexample, the set of unused signal dimensions can comprise one or moreunused cyclic shifts of a root sequence. If analyzed by hardwareprocessor 424, energy metrics associated with these unused cyclic shiftscan be averaged to estimate the noise for the PRACH. If analyzed bysoftware processing module 426, a highest energy metric for the rootsequence output by hardware processor 424 can be employed for theestimate of the noise, instead.

The aforementioned systems or apparatuses have been described withrespect to interaction between several components, modules and/orcommunication interfaces. It should be appreciated that such systems andcomponents/modules/interfaces can include those components/modules orsub-modules specified therein, some of the specified components/modulesor sub-modules, and/or additional modules. For example, a wirelesscommunication system could include base station 102 coupled with noiseestimation apparatus 200, or a different combination of these or otherentities. Sub-modules could also be implemented as modulescommunicatively coupled to other modules rather than included withinparent modules. Additionally, it should be noted that one or moremodules could be combined into a single module providing aggregatefunctionality. For instance, reference module 112 can includecalculation module 116, or vice versa, to facilitate identifying unusedsignal dimensions and employing those dimensions for channel noiseestimates, by way of a single component. The components can alsointeract with one or more other components not specifically describedherein but known by those of skill in the art.

Furthermore, as will be appreciated, various portions of the disclosedsystems above and methods below may include or consist of artificialintelligence or knowledge or rule based components, sub-components,processes, means, methodologies, or mechanisms (e.g., support vectormachines, neural networks, expert systems, Bayesian belief networks,fuzzy logic, data fusion engines, classifiers . . . ). Such components,inter alia, and in addition to that already described herein, canautomate certain mechanisms or processes performed thereby to makeportions of the systems and methods more adaptive as well as efficientand intelligent.

In view of the exemplary systems described supra, methodologies that maybe implemented in accordance with the disclosed subject matter will bebetter appreciated with reference to the flow charts of FIGS. 5-6A.While for purposes of simplicity of explanation, the methodologies areshown and described as a series of blocks, it is to be understood andappreciated that the claimed subject matter is not limited by the orderof the blocks, as some blocks may occur in different orders and/orconcurrently with other blocks from what is depicted and describedherein. Moreover, not all illustrated blocks may be required toimplement the methodologies described hereinafter. Additionally, itshould be further appreciated that the methodologies disclosedhereinafter and throughout this specification are capable of beingstored on an article of manufacture to facilitate transporting andtransferring such methodologies to computers. The term article ofmanufacture, as used, is intended to encompass a computer programaccessible from any computer-readable device, device in conjunction witha carrier, or storage medium.

FIG. 5 illustrates a flowchart of an example methodology 500 accordingto one or more aspects of the subject disclosure. At 502, method 500 cancomprise identifying a set of time-frequency resources for a randomaccess probe according to a wireless network protocol. At 504, method500 can comprise identifying at least one unused dimension that isorthogonal or pseudo-orthogonal to permitted random access probedimensions of the wireless network. At 506, method 500 can furthercomprise estimating noise for the random access probe based on analysisof the unused dimension.

According to specific aspects of the subject disclosure, identifying theat least one unused dimension can further comprise employing an unusedcyclic shift of a root sequence (e.g., a sequence providingpseudo-orthogonality for the random access probe) for the unuseddimension, wherein the root sequence provides a set of permitted randomaccess probe dimensions. In one option of this aspect, identifying theat least one unused dimension can further comprise employing a set ofunused cyclic shifts of the root sequence for the unused dimension.

As an alternative to the foregoing, identifying the at least one unuseddimension can further comprise employing an unused sequence of a rootsequence for the unused dimension. According to this latter aspect,method 500 can additionally comprise implementing the unused sequence ina hardware signal processor for estimating the noise. Furthermore,according to a sub-aspect, method 500 can comprise deriving energymetrics per shift within the unused sequence and performing an averageof the energy metrics, wherein an estimate of noise is obtained from theaverage of the energy metrics. As an alternative to this sub-aspect,method 500 can instead comprise implementing the unused sequence in asoftware signal processing algorithm for estimating the noise. For thisalternative, method 500 can optionally comprise deriving a set of energymetrics from a set of unused sequences that are employed for the unuseddimension, wherein estimating the noise further comprises averaging theset of energy metrics. In one other aspect of the subject disclosure,method 500 can comprise employing an unused root sequence for the unuseddimension. In this other aspect, method 500 can additionally comprisederiving a set of energy metrics per cyclic shift of the unused rootsequence and averaging the set of energy metrics for estimating thenoise.

As described, method 500 can provide substantial benefit for noiseestimation in a wireless network. For instance, employing unused signaldimensions can alleviate a need to separate actual signal transmissionsof a transmitting device from noise on an access channel. Furthermore,various aspects provide estimation of noise in receiver hardware forhigh accuracy estimation over a plurality of signal dimensions, or anestimation of noise in software to provide noise estimation with minimalcost or update time to conventional wireless receivers.

FIGS. 6 and 6A depict a flowchart of a sample methodology 600 accordingto additional aspects of the subject disclosure, where samplemethodology 600A refers to a portion of sample methodology 600. At 602,method 600 can comprise identifying an impending PRACH time slot for awireless network. At 604, method 600 can comprise employing a protocolof the wireless network to identify an unused signal dimension for noiseestimation for the impending PRACH time slot. At 606, method 600 cancomprise making a determination as to whether multiple sequences or asingle sequence is employed to generate the unused signal dimension. Ifmultiple sequences are employed, method 600 can proceed to FIG. 6A at602A. Otherwise, method 600 can proceed to 608.

At 608, method 600 can comprise estimating energy metrics of a set ofunused cyclic shifts of a single sequence employed for the unused signaldimension. At 610, method 600 can comprise determining whether anestimate of noise on the unused cyclic shifts is performed in a hardwaremodule or a software module. For hardware estimation, method 600 canproceed to 612; otherwise, method 600 proceeds to 616.

At 612, method 600 can comprise averaging energy metrics of the set ofunused cyclic shifts. At 614, method 600 can comprise measuring anenergy level of the average energy metric. From 614, method 600 canproceed to 620, where method 600 can employ the measured energy level toestablish a FA threshold for the PRACH.

At 616, method 600 can comprise outputting a highest energy estimate forthe sequence. At 618, method 600 can comprise measuring an energy levelof the output highest energy estimate. From 618, method 600 can proceedto 620, where the measured energy level is employed to establish the FAthreshold for the PRACH.

Referring now to FIG. 6A, method 600 can comprise measuring energylevels of cyclic shifts of respective sequences of multiple sequencesemployed for the unused signal dimensions, at 602A. At 604A, method 600can comprise determining whether an estimate of noise is implemented inthe hardware module or the software module. For the hardware module,method 600 can proceed to 606A; otherwise method 600 proceeds to 612A.

At 606A, method 600 can comprise averaging cyclic shift energy estimatesper cyclic shift and of respective sequences of the multiple sequences.At 608A, method 600 can comprise averaging the energy metrics across themultiple sequences. Additionally, at 610A, method 600 can comprisemeasuring the energy metric averaged over cyclic shift and over therespective sequences. From 610A, method 600 can proceed to 618A, wheremeasured energy is employed for establishing the FA threshold for thePRACH.

At 612A, method 600 can comprise outputting a highest energy metric persequence among respective energy metrics of cyclic shifts of eachsequence. Furthermore, at 614A, method 600 can comprise averaging theoutput highest energy metrics among the respective sequences. At 616A,method 600 can comprise measuring the averaged output highest energymetrics and, at 618A, method 600 can comprise employing the measuredenergy to establish the FA threshold for the PRACH.

FIG. 7 illustrates an example apparatus 700 for implementing accesschannel noise estimation in wireless communications according to aspectsof the subject disclosure. For instance, apparatus 700 can reside atleast partially within a wireless communication network and/or within awireless receiver such as a node, base station, access point, userterminal, personal computer coupled with a mobile interface card, or thelike. It is to be appreciated that apparatus 700 is represented asincluding functional blocks, which can be functional blocks thatrepresent functions implemented by a processor, software, or combinationthereof (e.g., firmware).

Apparatus 700 can comprise memory 702 for storing modules orinstructions configured to provide functions of apparatus 700, and adata processor 710 to execute modules that implement those functions.Particularly, apparatus 700 can comprise a module 704 for identifyingtime-frequency resources provided for uplink random access requestsaccording to a wireless network protocol. Further, apparatus 700 canadditionally comprise a module 706 for identifying signal dimensionsthat are orthogonal or pseudo-orthogonal to permitted random accessdimensions on the time-frequency resources, and that are not assignedfor uplink transmission on the time-frequency resources in a geographicregion served by the apparatus. Moreover, apparatus 700 can alsocomprise a module 708 for estimating noise on the time-frequencyresources based on analysis of the signal dimensions.

FIG. 8 depicts a block diagram of an example system 800 that canfacilitate wireless communication according to some aspects disclosedherein. On a DL, at access point 805, a transmit (TX) data processor 810receives, formats, codes, interleaves, and modulates (or symbol maps)traffic data and provides modulation symbols (“data symbols”). A symbolmodulator 815 receives and processes the data symbols and pilot symbolsand provides a stream of symbols. A symbol modulator 815 multiplexesdata and pilot symbols and provides them to a transmitter unit (TMTR)820. Each transmit symbol can be a data symbol, a pilot symbol, or asignal value of zero. The pilot symbols can be sent continuously in eachsymbol period. The pilot symbols can be frequency division multiplexed(FDM), orthogonal frequency division multiplexed (OFDM), time divisionmultiplexed (TDM), code division multiplexed (CDM), or a suitablecombination thereof or of like modulation and/or transmissiontechniques.

TMTR 820 receives and converts the stream of symbols into one or moreanalog signals and further conditions (e.g., amplifies, filters, andfrequency upconverts) the analog signals to generate a DL signalsuitable for transmission over the wireless channel. The DL signal isthen transmitted through an antenna 825 to the terminals. At terminal830, an antenna 835 receives the DL signal and provides a receivedsignal to a receiver unit (RCVR) 840. Receiver unit 840 conditions(e.g., filters, amplifies, and frequency downconverts) the receivedsignal and digitizes the conditioned signal to obtain samples. A symboldemodulator 845 demodulates and provides received pilot symbols to aprocessor 850 for channel estimation. Symbol demodulator 845 furtherreceives a frequency response estimate for the DL from processor 850,performs data demodulation on the received data symbols to obtain datasymbol estimates (which are estimates of the transmitted data symbols),and provides the data symbol estimates to an RX data processor 855,which demodulates (e.g., symbol demaps), deinterleaves, and decodes thedata symbol estimates to recover the transmitted traffic data. Theprocessing by symbol demodulator 845 and RX data processor 855 iscomplementary to the processing by symbol modulator 815 and TX dataprocessor 810, respectively, at access point 805.

On the UL, a TX data processor 860 processes traffic data and providesdata symbols. A symbol modulator 865 receives and multiplexes the datasymbols with pilot symbols, performs modulation, and provides a streamof symbols. A transmitter unit 870 then receives and processes thestream of symbols to generate an UL signal, which is transmitted by theantenna 835 to the access point 805. Specifically, the UL signal can bein accordance with SC-FDMA requirements and can include frequencyhopping mechanisms as described herein.

At access point 805, the UL signal from terminal 830 is received by theantenna 825 and processed by a receiver unit 875 to obtain samples. Asymbol demodulator 880 then processes the samples and provides receivedpilot symbols and data symbol estimates for the UL. An RX data processor885 processes the data symbol estimates to recover the traffic datatransmitted by terminal 830. A processor 890 performs channel estimationfor each active terminal transmitting on the UL. Multiple terminals cantransmit pilot concurrently on the UL on their respective assigned setsof pilot sub-bands, where the pilot sub-band sets can be interlaced.

Processors 890 and 850 direct (e.g., control, coordinate, manage, etc.)operation at access point 805 and terminal 830, respectively. Respectiveprocessors 890 and 850 can be associated with memory units (not shown)that store program codes and data. Processors 890 and 850 can alsoperform computations to derive frequency and time-based impulse responseestimates for the UL and DL, respectively.

For a multiple-access system (e.g., SC-FDMA, FDMA, OFDMA, CDMA, TDMA,etc.), multiple terminals can transmit concurrently on the UL. For sucha system, the pilot sub-bands can be shared among different terminals.The channel estimation techniques can be used in cases where the pilotsub-bands for each terminal span the entire operating band (possiblyexcept for the band edges). Such a pilot sub-band structure would bedesirable to obtain frequency diversity for each terminal.

The techniques described herein can be implemented by various means. Forexample, these techniques can be implemented in hardware, software, or acombination thereof. For a hardware implementation, which can bedigital, analog, or both digital and analog, the processing units usedfor channel estimation can be implemented within one or more applicationspecific integrated circuits (ASICs), digital signal processors (DSPs),digital signal processing devices (DSPDs), programmable logic devices(PLDs), field programmable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described herein, or a combination thereof. Withsoftware, implementation can be through modules (e.g., procedures,functions, and so on) that perform the functions described herein. Thesoftware codes can be stored in memory unit and executed by theprocessors 890 and 850.

FIG. 9 illustrates a wireless communication system 900 with multiplebase stations 910 (e.g., wireless access points, wireless communicationapparatus) and multiple terminals 920 (e.g., ATs), such as can beutilized in conjunction with one or more aspects. A base station 910 isgenerally a fixed station that communicates with the terminals and canalso be called an access point, a Node B, or some other terminology.Each base station 910 provides communication coverage for a particulargeographic area or coverage area, illustrated as three geographic areasin FIG. 9, labeled 902 a, 902 b, and 902 c. The term “cell” can refer toa base station or its coverage area depending on the context in whichthe term is used. To improve system capacity, a BS geographicarea/coverage area can be partitioned into multiple smaller areas (e.g.,three smaller areas, according to cell 902 a in FIG. 9), 904 a, 904 b,and 904 c. Each smaller area (904 a, 904 b, 904 c) can be served by arespective base transceiver subsystem (BTS). The term “sector” can referto a BTS or its coverage area depending on the context in which the termis used. For a sectorized cell, the BTSs for all sectors of that cellare typically co-located within the base station for the cell. Thetransmission techniques described herein can be used for a system withsectorized cells as well as a system with un-sectorized cells. Forsimplicity, in the subject description, unless specified otherwise, theterm “base station” is used generically for a fixed station that servesa sector as well as a fixed station that serves a cell.

Terminals 920 are typically dispersed throughout the system, and eachterminal 920 can be fixed or mobile. Terminals 920 can also be called amobile station, user equipment, a user device, wireless communicationapparatus, an access terminal, a user terminal or some otherterminology. A terminal 920 can be a wireless device, a cellular phone,a personal digital assistant (PDA), a wireless modem card, and so on.Each terminal 920 can communicate with zero, one, or multiple basestations 910 on the downlink (e.g., FL) and uplink (e.g., RL) at anygiven moment. The downlink refers to the communication link from thebase stations to the terminals, and the uplink refers to thecommunication link from the terminals to the base stations.

For a centralized architecture, a system controller 930 couples to basestations 910 and provides coordination and control for base stations910. For a distributed architecture, base stations 910 can communicatewith one another as needed (e.g., by way of a wired or wireless backhaulnetwork communicatively coupling the base stations 910). Datatransmission on the forward link often occurs from one access point toone access terminal at or near the maximum data rate that can besupported by the forward link or the communication system. Additionalchannels of the forward link (e.g., control channel) can be transmittedfrom multiple access points to one access terminal Reverse link datacommunication can occur from one access terminal to one or more accesspoints.

FIG. 10 is an illustration of a planned or semi-planned wirelesscommunication environment 1000, in accordance with various aspects.Wireless communication environment 1000 can comprise one or more basestations 1002 in one or more cells and/or sectors that receive,transmit, repeat, etc., wireless communication signals to each otherand/or to one or more mobile devices 1004. As illustrated, each basestation 1002 can provide communication coverage for a particulargeographic area, illustrated as four geographic areas, labeled 1006 a,1006 b, 1006 c and 1006 d. Each base station 1002 can comprise atransmitter chain and a receiver chain, each of which can in turncomprise a plurality of components associated with signal transmissionand reception (e.g., processors, modulators, multiplexers, demodulators,demultiplexers, antennas, and so forth, see FIG. 8, supra), as will beappreciated by one skilled in the art. Mobile devices 1004 can be, forexample, cellular phones, smart phones, laptops, handheld communicationdevices, handheld computing devices, satellite radios, globalpositioning systems, PDAs, or any other suitable device forcommunicating over wireless communication environment 1000. Wirelesscommunication environment 1000 can be employed in conjunction withvarious aspects described herein in order to facilitate multi-node relayassignment and cell-splitting effects in wireless communication, as setforth herein.

As used in the subject disclosure, the terms “component,” “system,”“module” and the like are intended to refer to a computer-relatedentity, either hardware, software, software in execution, firmware,middle ware, microcode, and/or any combination thereof. For example, amodule can be, but is not limited to being, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, a device, and/or a computer. One or more modules can residewithin a process, or thread of execution; and a module can be localizedon one electronic device, or distributed between two or more electronicdevices. Further, these modules can execute from variouscomputer-readable media having various data structures stored thereon.The modules can communicate by way of local or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, or across a network such as the Internet with othersystems by way of the signal). Additionally, components or modules ofsystems described herein can be rearranged, or complemented byadditional components/modules/systems in order to facilitate achievingthe various aspects, goals, advantages, etc., described with regardthereto, and are not limited to the precise configurations set forth ina given figure, as will be appreciated by one skilled in the art.

Furthermore, various aspects are described herein in connection with aUE. A UE can also be called a system, a subscriber unit, a subscriberstation, mobile station, mobile, mobile communication device, mobiledevice, remote station, remote terminal, AT, user agent (UA), a userdevice, or user terminal (UT). A subscriber station can be a cellulartelephone, a cordless telephone, a Session Initiation Protocol (SIP)phone, a wireless local loop (WLL) station, a personal digital assistant(PDA), a handheld device having wireless connection capability, or otherprocessing device connected to a wireless modem or similar mechanismfacilitating wireless communication with a processing device.

In one or more exemplary embodiments, the functions described can beimplemented in hardware, software, firmware, middleware, microcode, orany suitable combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes both computer storage media and communication mediaincluding any medium that facilitates transfer of a computer programfrom one place to another. A storage media may be any physical mediathat can be accessed by a computer. By way of example, and notlimitation, such computer storage media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, smart cards, and flash memory devices (e.g.,card, stick, key drive . . . ), or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio, and microwave areincluded in the definition of medium. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and blu-ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media.

For a hardware implementation, the processing units' variousillustrative logics, logical blocks, modules, and circuits described inconnection with the aspects disclosed herein can be implemented orperformed within one or more ASICs, DSPs, DSPDs, PLDs, FPGAs, discretegate or transistor logic, discrete hardware components, general purposeprocessors, controllers, micro-controllers, microprocessors, otherelectronic units designed to perform the functions described herein, ora combination thereof. A general-purpose processor can be amicroprocessor, but, in the alternative, the processor can be anyconventional processor, controller, microcontroller, or state machine. Aprocessor can also be implemented as a combination of computing devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other suitable configuration. Additionally, at least oneprocessor can comprise one or more modules operable to perform one ormore of the steps and/or actions described herein.

Moreover, various aspects or features described herein can beimplemented as a method, apparatus, or article of manufacture usingstandard programming and/or engineering techniques. Further, the stepsand/or actions of a method or algorithm described in connection with theaspects disclosed herein can be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.Additionally, in some aspects, the steps or actions of a method oralgorithm can reside as at least one or any combination or set of codesor instructions on a machine-readable medium, or computer-readablemedium, which can be incorporated into a computer program product. Theterm “article of manufacture” as used herein is intended to encompass acomputer program accessible from any suitable computer-readable deviceor media.

Additionally, the word “exemplary” is used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion. As usedin this application, the term “or” is intended to mean an inclusive “or”rather than an exclusive “or”. That is, unless specified otherwise, orclear from context, “X employs A or B” is intended to mean any of thenatural inclusive permutations. That is, if X employs A; X employs B; orX employs both A and B, then “X employs A or B” is satisfied under anyof the foregoing instances. In addition, the articles “a” and “an” asused in this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

Furthermore, as used herein, the terms to “infer” or “inference” refergenerally to the process of reasoning about or inferring states of thesystem, environment, or user from a set of observations as captured viaevents, or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents, or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources. What has been described above includes examplesof aspects of the claimed subject matter. It is, of course, not possibleto describe every conceivable combination of components or methodologiesfor purposes of describing the claimed subject matter, but one ofordinary skill in the art may recognize that many further combinationsand permutations of the disclosed subject matter are possible.Accordingly, the disclosed subject matter is intended to embrace allsuch alterations, modifications and variations that fall within thespirit and scope of the appended claims. Furthermore, to the extent thatthe terms “includes,” “has” or “having” are used in either the detaileddescription or the claims, such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A method for wireless communication, comprising:identifying a set of time-frequency resources for an uplink wirelesssignal according to a wireless network protocol; identifying at leastone unused dimension that is orthogonal or pseudo-orthogonal topermitted uplink wireless signal dimensions, the at least one unuseddimension comprising a root sequence and each cyclic shift of the rootsequence, the root sequence and each cyclic shift of the root sequencebeing unused; and estimating noise for the uplink wireless signal basedon analysis of at least one of the unused root sequence or the unusedcyclic shift of the unused root sequence.
 2. The method of claim 1,wherein the root sequence provides a set of permitted uplink wirelesssignal dimensions.
 3. The method of claim 1, wherein the root sequenceis a sequence providing pseudo-orthogonality for the uplink wirelesssignal.
 4. The method of claim 1, wherein the at least one of the unusedroot sequence or the unused cyclic shift of the unused root sequencecomprises the unused root sequence and each unused cyclic shift of theunused root sequence.
 5. The method of claim 4, further comprisingimplementing the estimating of the noise in a hardware signal processor.6. The method of claim 5, further comprising deriving energy metrics forthe unused root sequence and per unused cyclic shift of the unused rootsequence and performing an average of the energy metrics, wherein thenoise is obtained from the average of the energy metrics.
 7. The methodof claim 4, further comprising implementing the estimating of the noisein a software signal processing algorithm.
 8. The method of claim 7,further comprising deriving a set of energy metrics from a set of unusedsequences of the at least one unused dimension, and wherein theestimating of the noise further comprises averaging the set of energymetrics.
 9. The method of claim 1, further comprising deriving a set ofenergy metrics for the unused root sequence and per cyclic shift of theunused root sequence and averaging the set of energy metrics for theestimating of the noise.
 10. The method of claim 1, wherein the uplinkwireless signal is a random access probe.
 11. An apparatus for wirelesscommunication, comprising: means for identifying time-frequencyresources provided for an uplink wireless signal according to a wirelessnetwork protocol; means for identifying signal dimensions that areorthogonal or pseudo-orthogonal to permitted uplink wireless signaldimensions on the time-frequency resources, and that are not assignedfor uplink transmission on the time-frequency resources in a geographicregion served by the apparatus, the signal dimensions comprising a rootsequence and each cyclic shift of the root sequence; and means forestimating noise on the time-frequency resources based on analysis of atleast one of the root sequence or the cyclic shift of the root sequence.12. The apparatus of claim 11, wherein the root sequence provides a setof permitted uplink wireless signal dimensions.
 13. The apparatus ofclaim 11, wherein the root sequence is a sequence providingpseudo-orthogonality for the uplink wireless signal.
 14. The apparatusof claim 11, wherein the at least one of the root sequence or the cyclicshift of the root sequence comprises the root sequence and each cyclicshift of the root sequence.
 15. The apparatus of claim 11, wherein theuplink wireless signal comprises an uplink random access request. 16.The apparatus of claim 11, wherein the means for estimating the noise isimplemented in a hardware signal processor.
 17. The apparatus of claim16, further comprising means for deriving energy metrics for the rootsequence and per cyclic shift of the root sequence and performing anaverage of the energy metrics, wherein the noise is obtained from theaverage of the energy metrics.
 18. An apparatus for wirelesscommunication, comprising a processor configured to execute computerexecutable modules stored in memory, the modules including: a firstmodule that identifies time-frequency resources provided for an uplinkwireless signal according to a wireless network protocol; a secondmodule that identifies signal dimensions that are orthogonal orpseudo-orthogonal to permitted uplink wireless signal dimensions on thetime-frequency resources, and that are not assigned for uplinktransmission on the time-frequency resources in a geographic regionserved by a base station, the signal dimensions comprising a rootsequence and each cyclic shift of the root sequence; and a third modulethat estimates noise on the time-frequency resources based on analysisof at least one of the root sequence or the cyclic shift of the rootsequence.
 19. The apparatus of claim 18, wherein the root sequenceprovides a set of permitted uplink wireless signal dimensions.
 20. Theapparatus of claim 18, wherein the root sequence is a sequence providingpseudo-orthogonality for the uplink wireless signal.
 21. The apparatusof claim 18, wherein the at least one of the root sequence or the cyclicshift of the root sequence comprises the root sequence and each cyclicshift of the root sequence.
 22. The apparatus of claim 18, wherein theuplink wireless signal comprises an uplink random access request. 23.The apparatus of claim 18, wherein the third module is implemented in ahardware signal processor.
 24. The apparatus of claim 23, wherein thethird module is configured to derive energy metrics for the rootsequence and per cyclic shift of the root sequence and perform anaverage of the energy metrics, wherein the noise is obtained from theaverage of the energy metrics.
 25. A non-transitory computer-readablemedium storing computer executable code for wireless communication,comprising: a first set of codes for causing a computer to identifytime-frequency resources provided for an uplink wireless signalaccording to a wireless network protocol; a second set of codes forcausing the computer to identify signal dimensions that are orthogonalor pseudo-orthogonal to permitted uplink wireless signal dimensions onthe time-frequency resources, and that are not assigned for uplinktransmission on the time-frequency resources in a geographic regionserved by a base station, the signal dimensions comprising a rootsequence and each cyclic shift of the root sequence; and a third set ofcodes for causing the computer to estimate noise on the time-frequencyresources based on analysis of at least one of the root sequence or thecyclic shift of the root sequence.
 26. The non-transitorycomputer-readable medium of claim 25, wherein the root sequence providesa set of permitted uplink wireless signal dimensions.
 27. Thenon-transitory computer-readable medium of claim 25, wherein the rootsequence is a sequence providing pseudo-orthogonality for the uplinkwireless signal.
 28. The non-transitory computer-readable medium ofclaim 25, wherein the at least one of the root sequence or the cyclicshift of the root sequence comprises the root sequence and each cyclicshift of the root sequence.
 29. The non-transitory computer-readablemedium of claim 25, wherein the uplink wireless signal is an uplinkrandom access request.